# Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?

arXiv: https://arxiv.org/abs/2309.06036 · HTML: https://mlsanity.com/paper/2309.06036/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2023.

## Built on by (most cited first, 13 of 13)

- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception](https://mlsanity.com/paper/2501.15394/) (2025)
- [Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension](https://mlsanity.com/paper/2405.12821/) (2024)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [NanoMVG: USV-Centric Low-Power Multi-Task Visual Grounding based on Prompt-Guided Camera and 4D mmWave Radar](https://mlsanity.com/paper/2408.17207/) (2024)
- [ProbRadarM3F: mmWave Radar based Human Skeletal Pose Estimation with Probability Map Guided Multi-Format Feature Fusion](https://mlsanity.com/paper/2405.05164/) (2024)
- [4D mmWave Radar for Sensing Enhancement in Adverse Environments: Advances and Challenges](https://mlsanity.com/paper/2503.24091/) (2025)
- [On the Federated Learning Framework for Cooperative Perception](https://mlsanity.com/paper/2404.17147/) (2024)
- [OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering](https://mlsanity.com/paper/2503.12968/) (2025)
- [Multi-Object Tracking based on Imaging Radar 3D Object Detection](https://mlsanity.com/paper/2406.01011/) (2024)
- [CORT: Class-Oriented Real-time Tracking for Embedded Systems](https://mlsanity.com/paper/2407.17521/) (2024)
- [Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain](https://mlsanity.com/paper/2604.14013/) (2026)
- [4D Radar Perception Algorithms for Autonomous Driving: A Review](https://mlsanity.com/paper/2609.19216/) (2026)

## Abstract (arXiv)

Online 3D multi-object tracking (MOT) has recently received significant research interests due to the expanding demand of 3D perception in advanced driver assistance systems (ADAS) and autonomous driving (AD). Among the existing 3D MOT frameworks for ADAS and AD, conventional point object tracking (POT) framework using the tracking-by-detection (TBD) strategy has been well studied and accepted for LiDAR and 4D imaging radar point clouds. In contrast, extended object tracking (EOT), another important framework which accepts the joint-detection-and-tracking (JDT) strategy, has rarely been explored for online 3D MOT applications. This paper provides the first systematic investigation of the EOT framework for online 3D MOT in real-world ADAS and AD scenarios. Specifically, the widely accepted TBD-POT framework, the recently investigated JDT-EOT framework, and our proposed TBD-EOT framework are compared via extensive evaluations on two open source 4D imaging radar datasets: View-of-Delft and TJ4DRadSet. Experiment results demonstrate that the conventional TBD-POT framework remains preferable for online 3D MOT with high tracking performance and low computational complexity, while the proposed TBD-EOT framework has the potential to outperform it in certain situations. However, the results also show that the JDT-EOT framework encounters multiple problems and performs inadequately in evaluation scenarios. After analyzing the causes of these phenomena based on various evaluation metrics and visualizations, we provide possible guidelines to improve the performance of these MOT frameworks on real-world data. These provide the first benchmark and important insights for the future development of 4D imaging radar-based online 3D MOT algorithms.